浪潮人工智能工厂是国内首个面向行业场景的具备工业化、标准化、规模化能力的生产流水线，是用制造的模式来解决产业问题，即"模型制造模型"，就 是通过模型来打磨工具，再用工具来制造新的模型，从而形成开放高效的生产模式。同时，浪潮人工智能工厂通过打造数据车间、模型车间等"九大车 间"，能够重点解决人工智能全生命周期的标准化、自动化和规模化生产问题，充分释放数据要素价值。目前，浪潮人工智能工厂已沉淀61道工序、113套 工具，年满产1000+订单模型，并基于工匠中心持续提升生产能力与水平，交付周期从90人天缩短到20人天。 此外，人工智能工厂构建大模型产业生态体系孵化人工智能产业，既是人工智能行业垂类模型和智能体的制造平台，也是通用算力支撑平台，同时还是产 业链构建平台、生态创新孵化平台、科学技术研究平台、人才培育平台、产业投资平台，致力于人工智能发展和治理业务，实现大模型广泛应用。 国务院日前印发《关于深入实施"人工智能+"行动的意见》，提出加快实施6大重点行动，强化8项基础支撑能力，推动人工智能与经济社会各行业各领域 广泛深度融合。 人工智能作为引领新一轮科技革命和产业变革的颠覆性技术，已成为国际竞争的新焦点和经济 ...

Core Insights - The event held by the Shandong Provincial Government focused on promoting the "Shandong Good Brands on the Industrial Chain," highlighting the role of Inspur Group in the artificial intelligence (AI) sector [1] - Artificial intelligence is recognized as a strategic technology driving industrial upgrades and fostering new productive forces, with Shandong Province leveraging its industrial foundation and data resources to accelerate the AI industry chain [1][2] - Inspur Group aims to meet the demand for smaller, specialized, and decentralized computing power services through its AI factory, which is positioned as a leader in the AI industry chain in Shandong [1] Company Overview - The Inspur AI factory is the first in China designed for industrialized, standardized, and scalable production lines, consisting of a general computing power center, AI model factory, AI agent factory, and AI training ground [1][2] - The factory supports three product forms: city-level, industry-level, and enterprise-level, based on distributed intelligent cloud technology, facilitating the widespread application of large models [2] Production Capabilities - The AI factory has established 61 processes and 113 tools, with an annual output of over 1,000 model orders, significantly reducing delivery time from 90 person-days to 20 person-days [2] - It addresses the standardization, automation, and scalability of AI production throughout its lifecycle, maximizing the value of data elements [2] Strategic Goals - Inspur aims to provide a full-stack solution across the entire AI industry chain, enhancing ecological cooperation and industrial synergy to create a competitive "moat" in the AI field [2] - The company envisions a digital world where "intelligence is everywhere," driven by its innovative AI infrastructure and extensive industry experience [2]

Group 1 - The core viewpoint of the news is the introduction of Inspur's AI factory as a new type of infrastructure that meets the growing demand for specialized and decentralized computing power services in the AI industry [1][2] - Inspur's AI factory is the first in China to provide an industrialized, standardized, and scalable production line tailored for industry scenarios, integrating data, equipment, and research to create a comprehensive ecosystem for talent development, industry service, and technological innovation [1][2] - The AI factory has established nine major workshops to address standardization, automation, and scalability in the AI lifecycle, significantly enhancing the value of data elements [1][2] Group 2 - Inspur is recognized as the leading enterprise in Shandong's AI industry chain, focusing on large model technology innovation and application, and is the only company in China to offer a full-stack solution for generative AI across all five product and service layers [2] - The company has established a vast network with 122 cloud centers and 557 distributed cloud nodes nationwide, creating the largest distributed intelligent cloud in China, and collaborates with over 1,000 domestic and 400 overseas partners to facilitate the implementation of AI solutions [2] - The emphasis on ecological cooperation and industrial collaboration is highlighted, with the aim of building a competitive moat in the AI sector through partnerships with chip manufacturers, data processing firms, and model developers [3]

Group 1 - The emergence of generative AI has significantly increased the demand for computing power, transitioning from large models to intelligent agents and embodied intelligence [2] - The global AI server market is projected to grow from $125.1 billion in 2024 to $158.7 billion in 2025, reaching $222.7 billion by 2028, with generative AI servers' market share increasing from 29.6% in 2025 to 37.7% in 2028 [3] - In China, the intelligent computing power is expected to reach 1,037.3 EFLOPS by 2025 and 2,781.9 EFLOPS by 2028, with a compound annual growth rate (CAGR) of 46.2% from 2023 to 2028 [3] Group 2 - The trend of cross-domain and cross-cluster mixed training of large models is emerging, supported by advancements in computing network infrastructure [4] - The "East Data West Computing" initiative has seen over 43.5 billion yuan invested, with a total investment exceeding 200 billion yuan, improving network latency and energy efficiency [4] - The construction of computing networks is evolving towards AI-driven and distributed models, with a focus on multi-node and multi-mode collaboration [10] Group 3 - Companies face challenges in cross-cluster mixed training, particularly in integrating different computing service providers and ensuring effective communication protocols [5] - The shift in user demand from training to inference computing power is evident, indicating a transition from a scale-driven to an efficiency-driven industry [6][8] - The service model is evolving from traditional Infrastructure as a Service (IaaS) to Model as a Service (MaaS), focusing on industry-specific solutions [7] Group 4 - The increasing demand for computing power necessitates a reevaluation of self-built computing infrastructure, which may not be cost-effective for many companies [8] - Companies are increasingly opting for computing platforms to manage workloads, raising the bar for service providers to develop efficient scheduling platforms [9] - The construction of computing networks is crucial for driving innovation across various industries, with a focus on AI and distributed computing [9] Group 5 - The rise in computing demand also raises concerns about energy consumption in data centers, with AI data center capacity expected to grow at a CAGR of 40.5% by 2027 [12] - Innovative cooling technologies and strategic data center locations are being explored to reduce energy consumption [12][13] - The integration of AI technologies is enhancing the operational efficiency of data centers, leading to a shift towards fully automated "dark" data centers [15]